1. Technical Field
Example embodiments of the present invention relate in general to a method of detecting objects from images, and more specifically, to a method and apparatus for detecting and recognizing objects using vector histograms based on local binary patterns.
2. Related Art
Technology for detecting and recognizing objects from images has been widely applied to video surveillance systems, and lately, studies into an intelligent video surveillance (IVS) system for preventing intellectual crimes are actively conducted. In particular, an intelligent video surveillance system using image information was first applied to public service fields, and police stations, etc. already introduced forensic science using image information to greatly contribute to crime prevention and criminal arrests.
The IVS system is widely utilized in various places, such as airports, stations, parking lots, department stores, casinos, schools, etc., so that a large amount of video is recorded and monitored.
However, since the collected video is observed and monitored with naked eyes by monitoring workers, it is difficult to properly execute real-time monitoring due to difficulty in concentrating caused by long-time monitoring. That is, relatively insufficient time and labor compared with a large amount of recorded video make it difficult to properly observe or analyze the recorded video.
Accordingly, if a function of automatically detecting actions defined as main events is provided to the IVS system, the problem may be easily solved. Also, if a function of recording the generation times of main events, the locations of event frames, etc. is added when providing an automatic alarm upon detection of events and abnormal behaviors, the IVS system may be effectively managed.
However, in some conventional systems in which a function of detecting persons has been installed, a false alarm is often produced, or the case of failing to detect an intruder is also often generated. The cases are caused when the systems misrecognize an object as a person and vice versa or fail to detect a person although properly detecting motion.
Such detection errors are directly associated with the performance of a detector installed in the corresponding system, and come from insufficient feature information about images used in the detector or poor performance of a detection algorithm.
Particularly, representative ones of various detection methods use Haar-like features, local binary patterns, etc.
The Haar-like features can provide sufficient information in the case of images such as a face having relatively predominant features using a combination of feature points with a simple shape, but has difficulties in detecting a person that appears significantly differently according to his/her clothes' color and shape, a gait, etc.
Also, a method of detecting a person in a specific region through local binary patterns is robust to changes in local lighting since it does not directly use gradation patterns.
However, since the method has no reliable features with respect to geometrical changes (movement, scaling, tiling, etc.) in boundary areas of a specific image, the method may fail to detect a region of specific shape in an environment of limited learning result data.